A Mixed-Integer Programming Model for Optimal Allocation of COVID-19 Vaccines in Davao City

With the emergence of COVID-19 in Davao City, the need to acquire herd immunity through vaccination is paramount in averting the further spread of the disease in addition to complying with health and safety protocols. This study presents a reformulation of Smalley et al.’s (2015) oral cholera vaccine—mixed-integer programming model (OCV-MIP) to fit the context of the COVID-19 vaccination campaign in the city for 5 years, with consideration of the possible need for annual revaccination, given limited supply and budget resources, to minimize COVID-19 cases further. The population is divided into subgroups with associated incidence rates serving as the basis for the optimal allocation of vaccines. Different ways of population stratification by some combinations of risk areas and age group divisions were explored. The results revealed that it is optimal to prioritize the vaccination of subgroups with the highest incidence rates. Keywords: forecasting · COVID-19 · Davao City · LINGO · Mixed-Integer Programming · Optimization · Philippines · SARS-CoV-2 · Vaccine

A cointegration analysis of rabies cases and weather components in Davao City, Philippines from 2006 to 2017.

Rabies is a lethal viral disease and dogs are the major disease reservoir in the Philippines. Spatio-temporal variations in environmental factors are known to affect disease dynamics. Some rabies-affected countries considered investigating the role of weather components in driving rabies cases and it has helped them to strategize their control efforts. In this study, cointegration analysis was conducted between the monthly reported rabies cases and the weather components, such as temperature and precipitation, to verify the effect of weather components on rabies incidence in Davao City, Philippines. With the Engle-Granger cointegration tests, we found that rabies cases are cointegrated into each of the weather components. It was further validated, using the Granger causality test, that each weather component predicts the rabies cases and not vice versa. Moreover, we performed the Johansen cointegration test to show that the weather components simultaneously affect the number of rabies cases, which allowed us to estimate a vector-error correction model for rabies incidence as a function of temperature and precipitation. Our analyses showed that canine rabies in Davao City was weather-sensitive, which implies that rabies incidence could be projected using established long-run relationship among reported rabies cases, temperature, and precipitation. This study also provides empirical evidence that can guide local health officials in formulating preventive strategies for rabies control and eradication based on weather patterns

Transmission dynamics and baseline epidemiological parameter estimates of Coronavirus disease 2019 pre-vaccination: Davao City, Philippines

The Coronavirus disease 2019 (COVID-19) has exposed many systemic vulnerabilities in many countries' health system, disaster preparedness, and adequate response capabilities. With the early lack of data and information about the virus and the many differing local-specific factors contributing to its transmission, managing its spread had been challenging. The current work presents a modified Susceptible-Exposed-Infectious-Recovered compartmental model incorporating intervention protocols during different community quarantine periods. The COVID-19 reported cases before the vaccine rollout in Davao City, Philippines, are utilized to obtain baseline values for key epidemiologic model parameters. The probable secondary infections (i.e., time-varying reproduction number) among other epidemiological indicators were computed. Results show that the cases in Davao City were driven by the transmission rates, positivity proportion, latency period, and the number of severely symptomatic patients. This paper provides qualitative insights into the transmission dynamics of COVID-19 along with the government's implemented intervention protocols. Furthermore, this modeling framework could be used for decision support, policy making, and system development for the current and future pandemics. Copyright: © 2023 Añonuevo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.11Nsciescopu